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Effective well placement and design planning accounts for subsurface uncertainties to estimate production and economic outcomes. Reservoir modelling and simulation workflows build on ensemble approaches to manage uncertainties for production forecasting. Ensemble generation and interpretation requires a higher degree of automation analytics and artificial intelligence for fast value extraction and decision support. This work develops practical intelligent workflow steps for a robust infill well placement and design scenario in multi-layered/stacked reservoirs under uncertainty. Potential well targets are classified by an opportunity index defined by a combination of rock and hydrocarbon flow properties as well as connected volumes above a minimum economic volume. Unsupervised learning techniques are applied to automate the search for alternative target areas, so-called hotspot regions. Supervised machine/learning models are used to predict infill well performance based on simulated and/or past production experience. A stochastic evaluation including all ensemble cases is used to capture uncertainty. Vertical, deviated, horizontal and multilateral wells are proposed to optimally target single or connect to multiple hotspot regions under technical and economic constraints. A structured workflow design is applied to a multi-layered/stacked reservoir model. Subsurface uncertainties are described and captured by multiple model realizations, which are constrained in areas of historical wells. An infill well program for a multi-layered/stacked reservoir is defined for incremental production increase under economic constraints. This work shows how robust well location and design builds on the full ensemble of cases with a high degree of automation using analytics and machine-learning techniques. Both production and economic targets are calculated and compared to a reference case for robust solution verification and probability of success. In conclusion, an overall reservoir-driven field development strategy is required for efficient execution. However, automation is well applicable to repetitive workflow steps which includes hotspot search in an ensemble of validated reservoir models. This work presents an integrated, intelligent solution for informed decision making on infill drilling locations and refined well design. Higher degree of automation with embedded intelligence are discussed from case generation to hotspot identification. Aspects of model calibration in a producing field environment are addressed.
Effective well placement and design planning accounts for subsurface uncertainties to estimate production and economic outcomes. Reservoir modelling and simulation workflows build on ensemble approaches to manage uncertainties for production forecasting. Ensemble generation and interpretation requires a higher degree of automation analytics and artificial intelligence for fast value extraction and decision support. This work develops practical intelligent workflow steps for a robust infill well placement and design scenario in multi-layered/stacked reservoirs under uncertainty. Potential well targets are classified by an opportunity index defined by a combination of rock and hydrocarbon flow properties as well as connected volumes above a minimum economic volume. Unsupervised learning techniques are applied to automate the search for alternative target areas, so-called hotspot regions. Supervised machine/learning models are used to predict infill well performance based on simulated and/or past production experience. A stochastic evaluation including all ensemble cases is used to capture uncertainty. Vertical, deviated, horizontal and multilateral wells are proposed to optimally target single or connect to multiple hotspot regions under technical and economic constraints. A structured workflow design is applied to a multi-layered/stacked reservoir model. Subsurface uncertainties are described and captured by multiple model realizations, which are constrained in areas of historical wells. An infill well program for a multi-layered/stacked reservoir is defined for incremental production increase under economic constraints. This work shows how robust well location and design builds on the full ensemble of cases with a high degree of automation using analytics and machine-learning techniques. Both production and economic targets are calculated and compared to a reference case for robust solution verification and probability of success. In conclusion, an overall reservoir-driven field development strategy is required for efficient execution. However, automation is well applicable to repetitive workflow steps which includes hotspot search in an ensemble of validated reservoir models. This work presents an integrated, intelligent solution for informed decision making on infill drilling locations and refined well design. Higher degree of automation with embedded intelligence are discussed from case generation to hotspot identification. Aspects of model calibration in a producing field environment are addressed.
Rubiales is a major heavy oil field in Colombia with an OOIP larger than 5000 MSTB (Stanko, and others, 2015). The field produces from six zones mainly with horizontal wells. Production is driven by a strong aquifer which causes tilted oil-water-contact and early water breakthrough. Fully integrated reservoir modelling for field development optimization under subsurface uncertainty has been a major challenge so far. This paper presents an automated calibration process, probabilistic infill well ranking and location optimization. An automated reservoir characterization workflow was developed to generate multiple history matched models on field and well level. Static reservoir characteristics and contacts where parameterized for sensitivity assessments and calibration update steps. Variations of dynamic reservoir characteristics with an impact on model forecasting behavior were applied to alternative history matching solutions to create an ensemble of reservoir models for uncertainty assessment. Economic success criteria and a simulation opportunity index were defined for a probabilistic well ranking and optimized well location assessment. The workflow was applied to a sector of the full field including approximately 300 producer wells. Multiple history match solutions were created with 80% of the producer wells matching on well level. Quality assurance measures were applied to verify geological consistency of implemented model updates. The ensemble of forecasting models was used to deliver a probabilistic well ranking based on a well Net Present Value model. Infill well candidates with a robust performance delivery across the ensemble were identified. Results showed that a well placement scenario with half of more than 100 well candidates delivered above the economic threshold criterion and a similar recovery compared to reference field development plan. Probabilistic sweet spot maps based on a simulation opportunity index were used to efficiently identify well locations for more than 30 alternatives well candidates. The method produced robust results above the economic success criterion. Methodology and workflow design developed in this work successfully delivered a field development evaluation under subsurface uncertainty for a large heavy oil field with complex geological characteristics, long production history and large number of wells. The workflow design is applicable for other fields with similar characteristics and delivery objectives. The developing of this advanced workflow combined the application of a last-generation High-Resolution Reservoir Simulator (HRRS) and an Innovative Collaboration Environment (ICE) (Schlumberger 2020) which combines domain expertise and advanced digital technologies (ADT) enhanced quality and time results for history matching (HM) scenarios and bring the opportunity to execute several uncertainty cases for forecasting analysis allowing us to consider a wide range of results for final FDP proposed
A small onshore brownfield in south Oman has low oil recovery because of its heavy oil and high water production, which together with reservoir uncertainties poses development challenges. Petrogas implemented an innovative field development planning approach to quantitatively compare multiple field development scenarios and optimize the operational choices within each. The workflow started with a single history matched model for each of the two geological structures in the field. A set of 14 field development scenarios were defined, on injection rates, well locations, and injection fluids. Identification and quantification of subsurface uncertainties were performed. These uncertainties were included in the geomodel for each scenario, which generated an ensemble of realizations and corresponding production forecasts. Two sets of economic results were produced—a simple, discounted cashflow model and the fiscal terms of the operator's service contract. Each ensemble was run against these models to generate probabilistic performance indicators for each scenario. Using cloud-computing capability, the field development study was drastically accelerated without losing on the quality. Almost 800 simulations were run over 5 days, covering 32 development scenarios in total (for two structures), automatically integrated with the economics workflow, providing in-depth analyses. The scenarios were compared in a series of dashboards that presented the economic metrics and their corresponding cumulative distribution functions. The analysis yielded several important insights: longer wells did not provide enough additional production to offset the increased costs. Moreover, peripheral drive with horizontal wells was more effective than irregular vertical wells. The waterflood scenarios improved production, but the polymer-injection option with short horizontal wells and peripheral infill well pattern was the highest-performing scenario. The study also helped identify areas where more detailed optimization studies should be performed, e.g., to optimize polymer-injection scheduling and polymer design. Traditionally, subsurface uncertainties analysis was restricted to a small number of discrete model realizations. Results were quantified in terms of production ranges only. Here, production forecasts were based on an ensemble of models, capturing the full range of uncertainties. In addition, evaluation criteria included economics.
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